Model instability in predictive exchange rate regressions

Niko Hauzenberger and Florian Huber

Papers from arXiv.org

Abstract: In this paper we aim to improve existing empirical exchange rate models by accounting for uncertainty with respect to the underlying structural representation. Within a flexible Bayesian non-linear time series framework, our modeling approach assumes that different regimes are characterized by commonly used structural exchange rate models, with their evolution being driven by a Markov process. We assume a time-varying transition probability matrix with transition probabilities depending on a measure of the monetary policy stance of the central bank at the home and foreign country. We apply this model to a set of eight exchange rates against the US dollar. In a forecasting exercise, we show that model evidence varies over time and a model approach that takes this empirical evidence seriously yields improvements in accuracy of density forecasts for most currency pairs considered.

Date: 2018-11, Revised 2018-12
New Economics Papers: this item is included in nep-for
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Model instability in predictive exchange rate regressions (2020)
Working Paper: Model instability in predictive exchange rate regressions (2018)
Working Paper: Model instability in predictive exchange rate regressions (2018)
Working Paper: Model instability in predictive exchange rate regressions (2018)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1811.08818

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